Integration of Scientific, Rule-based, Example-based, and Heuristic Know-How
High-Dimensional System Identification
Nonlinear Parameter Identification
Time Series Prediction
Data Driven Processes
Data Driven Business
Data Driven Anything
Data = Examples = Experience
> Computer-Aided Example-Based Modeling (= CAEBM) can analyze and explain arbitrary, interdisciplinary, high-dimendinal problems from any domain, employing models with as well numeric as nonnumeric parameters.
> The associative modeling is done based exclusively on examples, using "Genetic Neural Nets". No theories, no assumptions, no prejudices, no additional verifications are required: The problem-examples alone, collected from any source, generate the models, with built-in analysis of model complexity and accuracy.
> The resulting highly portable models can "explain" any relationship details between the participating parameters in 1 to 3 of many dimensions, making the models understandable by and plausible for "1-3 dimensional" human users.
> As practically anything on earth can be seen as (a sequence of) examples, CAEBM can use the myriads of examples available, to detect the know-how contained, and to make it re-usable for humans and computers.
> In general, CAEBM can expand considerably the Experience-based problem understanding capabilities of humans, restricted normally to max 1-3 dimensions, the same way like Computer Aid (CA) has done it so impressively for Science-based problem understanding since several decades. A new fruitful balance between "Experience" and "Science" becomes possible.
> Know-how is the most important resource in today's businesses and organizations.
> Know-how is distributed in people's heads, in science-based models, in test results, in rules, in example-collections etc.
> Any know-how from any source, and anything (happening) in this world can be understood and represented by (a sequence of) examples, with numeric and nonnumeric parameters, if appropriate.
> Our Example-Based Modeling (EBM) technology constructs high-dimensional parametric models, from examples only. Neural Nets are used as modeling kernel and Genetic Algorithms setup the models and train them.
> We use Computer Aid (CA) for example preparation, iterative model setup, generalization & quality assurance, and parameter set optimization, resulting in our CAEBM technology: EBM+CA=CAEBM.
> Our results are handy computer models, containing the example-based know-how from any source in arbitrary problem domains, defined by the parameter set in use, error measures included.
> Our CAEBM technology can be used to collect, consolidate, continuously refine, and systematically RE-USE any know-how in any problem domain.
> Our CAEBM technology can construct new solutions for high-dimendional, interdisciplinary, even scientifically unresolvable problems, to make them understandable at the same time for human brains, normally restricted to max 2-3 dim problem understanding.
> Our CAEBM technology makes know-how becoming a portable, tradable product, independent from the know-how sources. This allows for totally new business opportunities, eg concentrating on know-how collection and refinement in their special problem domains. And until now "impossible" problem solutions become feasible.***
*** Design of predictive local, regional, national etc Covid-19 strategies, based on worldwide examples of infections and contra-measures taken, minimizing the damage done to people's health, to personal freedom, and to economy, while avoiding pandemic developments.
*** Design of a low-cost, self-learning, local, regional, national etc weather prediction network, based on the weather itself as example source, making short to long-time predictions as needed.
*** Design of a self-learning, inter-modal, local, regional, national etc traffic control network, based on the traffic itself as example source, minimizing traffic burden + maximizing transport performance for a given infrastructure, and identifying cost-minimal bottle-neck eliminations + most cost-efficient infrastructure developments.
*** Setup of "intelligent", self-learning test stands, which collect their experiences gained so far by CAEBM technology in test stand-specific models (TSSMs), ready then to do additional test jobs in a fraction of time, because most often new test jobs can be mostly fullfiled by the TSSMs, and only a few additional test stand runs are needed, to "calibrate" the experience to the new test job.
*** Setup and maintenance of a know-how network of CAEBMs for the development of a family of products (eg a family of cars), to be used for super-fast development (and production) of customer-individual products.
*** and many more...